Analysis of chronic kidney disease among national hospitalization data with 14 million children

Shi, Xinmiao; Shi, Ying; Zhang, Luxia; Gan, Lanxia; Zhong, Xing; Huang, Yuming; Yao, Chen; Wang, Yanfang; Dong, Chongya; Liu, Beini; Wang, Fang; Wang, Haibo; Ding, Jie

doi:10.1186/s12882-021-02383-1

Cited by 15 publications

(12 citation statements)

References 27 publications

(26 reference statements)

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“…In the present study, we further refined this approach with the goal to design a more accurate and robust eGFR equation, relative to tracer measured GFR as gold standard reference, for an adult population, which encompasses the highest risk group for CKD [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Estimating Glomerular Filtration Rate from Serum Myo-Inositol, Valine, Creatinine and Cystatin C

Grassi¹,

Meeusen

Lieske

et al. 2021

Diagnostics

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Assessment of renal function relies on the estimation of the glomerular filtration rate (eGFR). Existing eGFR equations, usually based on serum levels of creatinine and/or cystatin C, are not uniformly accurate across patient populations. In the present study, we expanded a recent proof-of-concept approach to optimize an eGFR equation targeting the adult population with and without chronic kidney disease (CKD), based on a nuclear magnetic resonance spectroscopy (NMR) derived ‘metabolite constellation’ (GFRNMR). A total of 1,855 serum samples were partitioned into development, internal validation and external validation datasets. The new GFRNMR equation used serum myo-inositol, valine, creatinine and cystatin C plus age and sex. GFRNMR had a lower bias to tracer measured GFR (mGFR) than existing eGFR equations, with a median bias (95% confidence interval [CI]) of 0.0 (−1.0; 1.0) mL/min/1.73 m2 for GFRNMR vs. −6.0 (−7.0; −5.0) mL/min/1.73 m2 for the Chronic Kidney Disease Epidemiology Collaboration equation that combines creatinine and cystatin C (CKD-EPI2012) (p < 0.0001). Accuracy (95% CI) within 15% of mGFR (1-P15) was 38.8% (34.3; 42.5) for GFRNMR vs. 47.3% (43.2; 51.5) for CKD-EPI2012 (p < 0.010). Thus, GFRNMR holds promise as an alternative way to assess eGFR with superior accuracy in adult patients with and without CKD.

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Section: Introductionmentioning

confidence: 99%

Estimating Glomerular Filtration Rate from Serum Myo-Inositol, Valine, Creatinine and Cystatin C

Grassi¹,

Meeusen

Lieske

et al. 2021

Diagnostics

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“…There are up to 353 variables including patient demographics, clinical diagnosis coded according to the International Classification of Disease, 10th revision (ICD-10), medical payment methods, and information on hospitals on every record. Owing to the big data stored in the HQMS, numerous studies have been published on CKD (21)(22)(23), cardiovascular diseases (24), and other diseases (25).…”

Section: Patients Characteristicsmentioning

confidence: 99%

“…China has a large CKD population (1,2,21), therefore, research on risk factors for the progression of CKD in Chinese children is helpful to reveal the potential risk factors for children CKD progression in Asia. We, therefore, conducted a cohort study based on Hospital Quality Monitoring System (HQMS), a national inpatient database in China, to evaluate the association between risk factors and progression to CKD stage 5 in CKD children with glomerular etiology.…”

Section: Introductionmentioning

confidence: 99%

Risk Factors for Progression of Chronic Kidney Disease With Glomerular Etiology in Hospitalized Children

Li²,

Zhong³

et al. 2021

Front. Pediatr.

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Aim: To Identify association between risk factors to Chronic kidney disease (CKD) stage 5 in children with glomerular diseases in children in China.Methods: The Hospital Quality Monitoring System database was used to extract data for the study cohort. The primary outcome included progression to CKD stage 5 or dialysis. Cox regression was used to assess potential risk factors. Patients with lower stages (CKD stage 1 and 2) and higher stages (CKD stage 3 and 4) at baseline were analyzed separately.Results: Of 819 patients (4,089 hospitalization records), 172 (21.0%) patients reached the primary outcome during a median followed-up of 11.4 months. In the lower stages group, factors associated with the primary outcome included older age [Hazard Ratio (HR), 1.21; 95% confidence interval (CI), 1.10–1.34] and out-of-pocket payment (HR, 4.14; 95% CI, 1.57–10.95). In the higher stages group, factors associated with the primary outcome included CKD stage 4 (HR, 2.31; 95% CI, 1.48–3.62) and hypertension (HR, 1.99; 95% CI, 1.29–3.07). The medical migration rate was 38.2% in this study population.Conclusion: There are different risk factors for progression to the primary outcome in different stages in CKD with glomerular etiology. Further prospective studies are needed to assess these risk factors. The high medical migration rate reflected the regional disparities in the accessibility of pediatric kidney care between regions.

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“…Regarding the analyses of the entire dataset of chronic conditions (output found in Table B), significant relationships at p < .0001 were found between residential status most predictor variables, with the following exceptions: gender (both male and female) and age group (0-17). These significances largely held in the subset of data in the hospitalization dataset with only CKD, COPD, and obesity patients (output in Table C), except that age (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34) was not significant at p = 0.0035 and payor (Medicare) p = 0.0031; gender remained insignificant.…”

Section: Table 2 Hospitalization Dataset Sociodemographic Outcomes By...mentioning

confidence: 94%

“…In contrast, Croft et al (2018) found that prevalence, hospitalization, and mortality were higher for those with COPD living in rural areas as opposed to metropolitan and micropolitan areas in a Medicare enrollee population of 335,000 across multiple states. 31 Other articles also found that COPD mortality rates were higher for rural patients in specific populations through univariate analyses. 32,33 Readmission to the IP hospital is also a topic which COPD research is not short on.…”

Section: Chronic Obstructive Pulmonary Disease (Copd)mentioning

confidence: 96%

Assessing variance in inpatient hospitalization rates for three major chronic conditions

Mandy¹

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Introduction: A chronic condition is broadly defined as a health condition lasting one or more years that requires relatively consistent medical attention and/or imposes limitations on tasks and activities considered part of "daily living." Chronic and mental conditions comprised 90 percent of healthcare spending in 2019 coming to approximately $3.69 trillion. Given that the average inpatient (IP) hospitalization cost was $11,700 in 2017, investigating the demographic source of chronic IP hospitalizations is of high interest to reduce costs. However, current research is not consistent in how IP hospitalization rate is measured and does not control for known disparities in condition prevalence. In this study, rates of IP hospitalization are measured and compared across three levels of residential status (metropolitan, micropolitan, and rural) for chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), and obesity; these three conditions are further assessed to understand the degree to which residential status influences whether an individual who has one of the given conditions is hospitalized by controlling for condition prevalence by county. Methods: There were two main datasets utilized in this study, a primary individual-level dataset that includes primary and secondary diagnoses for individual IP hospitalizations and a county-level dataset that presents prevalence and hospitalization frequencies for each of the conditions. Using USDA rural-urban continuum codes, each county in Missouri was assigned a residential status value of metropolitan (1), micropolitan (2), and rural (3); this file was uploaded to the MUHC SQL Shared Analytics Server. A query was designed to pull individual-level IP hospitalization data for 17 variables of interest from a database within the Shared Analytics server such as demographic, diagnosis, and location information for all ICD-10 codes considered "chronic", per the AHRQ chronic condition indicator. IP hospitalization frequencies for each condition were extracted and input into a CDC county-level prevalence dataset. IP hospitalization frequencies were divided by prevalence frequencies for each county in Missouri to attain an IP hospitalization rate, which is defined in this study as the ratio of people who were hospitalized in the IP setting for a given condition out of the total number of people with that condition by county. Two univariate regression models (linear and logistic) were developed to assess the statistical significance and degree to which IP hospitalization rate for is impacted by residential status; these models were performed three times, once for each condition Results: Although the average prevalence rates for all three conditions was highest in the rural residential status, the average IP hospitalization rates were lower across the board in rural residential areas. Univariate logistic regression analyses found that rural residential status (reference = metropolitan) was significant at p [greater than] 0.05 with p = 0.0142 (parameter estimate = -3.3042); the linear regression analysis of COPD was not significant at p = 0/06. Analysis of regression outputs for CKD and obesity found no statistically significant p-values, although p-values were lower in both conditions (for both regressions) for rural than micropolitan, indicating a relationship with population. The other responses in logistic analyses, although not significant, trend in the direction of lower IP hospitalization rates for the rural residential status, aligning with the average rates. Linear univariate regression analyses found no statistically significant correlations. Discussion, Conclusion, and Recommendations: Whether or not residential status has implications for chronic disease prevalence is no longer largely contested in the current literature; however, the existence of disparities in IP hospitalization rates is still open for debate. While the findings here point to the fact that IP hospitalization rates for those with CKD, COPD, and obesity are lower across the board for patients of rural residential status, despite higher prevalence rates in Missouri during 2019, only rural status (reference = metropolitan) for COPD had a statistically significant difference at p [greater than] 0.05 with p = 0.0142. The major takeaway from this study should be that there is great need for a standardized measurement system for IP hospitalization rate, not just for CKD, COPD, and obesity, but likely for all conditions. The field of health informatics and organizations governing the research therein should collaborate to identify the optimal measure based off the evolution of knowledge that has been collected -- it makes little sense to continue pushing out research identifying 1) statistical significance with either no basis for normalization against population, and 2) normalization on population alone. The first and foremost recommendation is that a standardized measure of hospitalization rate be identified and implemented into recognized ontologies for future research by an overarching organization such as the AHRQ or CDC to better guide researchers and to make the methodology standardized in the United States. Second, as it is also known that access to healthcare, particularly specialized care, is lower in rural areas, research should be continued into the costs of CKD, COPD, and obesity and how rural residents might adversely be affected. Third, and perhaps mostly importantly from a long-term research goal perspective, strategies should continue to be developed that identify cost-reduction strategies for IP hospitalizations, especially for chronic conditions.

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Analysis of chronic kidney disease among national hospitalization data with 14 million children

Cited by 15 publications

References 27 publications

Estimating Glomerular Filtration Rate from Serum Myo-Inositol, Valine, Creatinine and Cystatin C

Estimating Glomerular Filtration Rate from Serum Myo-Inositol, Valine, Creatinine and Cystatin C

Risk Factors for Progression of Chronic Kidney Disease With Glomerular Etiology in Hospitalized Children

Assessing variance in inpatient hospitalization rates for three major chronic conditions

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